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EDWIN ROBERT PEREZ
Universidad Distrital Francisco Jose de Caldas
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3916-7305
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This article presents the results of the research work of the GIGA research group of the Distrital University in Bogota Colombia in the framework of the advanced doctoral research in the engineering doctorate of the Javeriana University and shows the development followed and the corresponding analysis determine the effectiveness of the M5P algorithm within the decision tree approach for the management of BIG DATA and Machine Learning for the determination of massive property valuation. The analysis was carried out with data provided by the Special Administrative Unit of the Distrital Cadaster (UAECD) and the Technical Cadastral Observatory corresponding to three sectors of the city of Bogotá D.C. The results of the J48-based model were compared by means of a statistical analysis with the traditionally used linear regression method, obtaining satisfactory results with errors well below the linear regressions in the training, validation and forecast stages.
Keywords
Machine learning, decision trees, Cross validation, Percentage Split, J48 algorithm, Linear Regression, BIG DATA, mass valuation, real estate
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CURRENT STATUS: Under Review
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Posted 14 Feb, 2020
No community comments so far
Editorial decision: Major Revision
On 30 Mar, 2020
Reviewer #11 agreed
On 24 Mar, 2020
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Reviewer #9 agreed
On 22 Mar, 2020
Reviewer #10 agreed
On 22 Mar, 2020
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Review #10 received
Received 22 Mar, 2020
Reviewer #3 agreed
On 20 Mar, 2020
Reviewer #4 agreed
On 20 Mar, 2020
Reviewer #5 agreed
On 20 Mar, 2020
Reviewer #6 agreed
On 20 Mar, 2020
Reviewer #7 agreed
On 20 Mar, 2020
Reviewer #8 agreed
On 20 Mar, 2020
Review #1 received
Received 20 Mar, 2020
Reviewers invited
Invitations sent on 20 Mar, 2020
Reviewer #1 agreed
On 20 Mar, 2020
Reviewer #2 agreed
On 20 Mar, 2020
First submitted
On 10 Feb, 2020
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Subject Areas
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This preprint is under consideration at Journal of Big Data. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
EDWIN ROBERT PEREZ
Universidad Distrital Francisco Jose de Caldas
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3916-7305
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 14 Feb, 2020
No community comments so far
Editorial decision: Major Revision
On 30 Mar, 2020
Reviewer #11 agreed
On 24 Mar, 2020
Review #6 received
Received 24 Mar, 2020
Review #11 received
Received 24 Mar, 2020
Review #4 received
Received 23 Mar, 2020
Review #3 received
Received 23 Mar, 2020
Review #2 received
Received 23 Mar, 2020
Review #5 received
Received 22 Mar, 2020
Reviewer #9 agreed
On 22 Mar, 2020
Reviewer #10 agreed
On 22 Mar, 2020
Review #9 received
Received 22 Mar, 2020
Review #10 received
Received 22 Mar, 2020
Reviewer #3 agreed
On 20 Mar, 2020
Reviewer #4 agreed
On 20 Mar, 2020
Reviewer #5 agreed
On 20 Mar, 2020
Reviewer #6 agreed
On 20 Mar, 2020
Reviewer #7 agreed
On 20 Mar, 2020
Reviewer #8 agreed
On 20 Mar, 2020
Review #1 received
Received 20 Mar, 2020
Reviewers invited
Invitations sent on 20 Mar, 2020
Reviewer #1 agreed
On 20 Mar, 2020
Reviewer #2 agreed
On 20 Mar, 2020
First submitted
On 10 Feb, 2020
Editor assigned
On 10 Feb, 2020
Submission checks complete
On 09 Feb, 2020
Editor invited
On 09 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Artificial Intelligence and Machine Learning
License:
This work is licensed under a CC BY 4.0 License. Read Full License
This article presents the results of the research work of the GIGA research group of the Distrital University in Bogota Colombia in the framework of the advanced doctoral research in the engineering doctorate of the Javeriana University and shows the development followed and the corresponding analysis determine the effectiveness of the M5P algorithm within the decision tree approach for the management of BIG DATA and Machine Learning for the determination of massive property valuation. The analysis was carried out with data provided by the Special Administrative Unit of the Distrital Cadaster (UAECD) and the Technical Cadastral Observatory corresponding to three sectors of the city of Bogotá D.C. The results of the J48-based model were compared by means of a statistical analysis with the traditionally used linear regression method, obtaining satisfactory results with errors well below the linear regressions in the training, validation and forecast stages.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
The full text of this article is available to read as a PDF.
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